人民长江2026，Vol.57Issue(2)：213-221,9.DOI:10.16232/j.cnki.1001-4179.2026.02.026
基于LSTM-TL的大坝变形监控模型研究
Research on dam deformation monitoring model based on LSTM-TL
摘要
Abstract
Aiming at the issue of low prediction accuracy caused by small sample sizes and irregular data in dam safety monito-ring models,this paper proposes a new deformation monitoring model that integrates transfer learning(TL)with a long short-term memory neural network(LSTM).The specific methodology is as follows:first,the source domain model and the target do-main model are constructed using finite element simulation data and measured data,respectively.Then,well-performing parame-ters are extracted from the source domain model and transferred to the target domain model as initial parameters.Finally,based on the measured data,the initial parameters of the target domain model are fine-tuned to obtain the optimal parameters,thereby gen-erating the final target domain monitoring model.Engineering application demonstrates that the LSTM-TL model can effectively suppress noise interference and overfitting induced by small sample of the data,significantly improving the model's accuracy.The regularity similarity(i.e.,non-numerical similarity)between the source domain and the target domain is the main factor influ-encing model accuracy.When the sample size is less than 150,the accuracy of the LSTM-TL model is significantly better than that of the traditional LSTM model;the performance of the two models becomes comparable when the sample size exceeds 150.The research findings can provide a reference for real-time dam safety monitoring and early warning.关键词
大坝变形/监控模型/长短期记忆网络(LSTM)/有限元/迁移学习(TL)/影响因素Key words
dam deformation/monitoring model/long-term and short-term neural networks(LSTM)/finite element/trans-fer learning(TL)/affecting factor分类
建筑与水利引用本文复制引用
王秀桃,田纪辰,李艳玲,裴亮,卢祥,陈天赐..基于LSTM-TL的大坝变形监控模型研究[J].人民长江,2026,57(2):213-221,9.基金项目
国家自然科学基金项目(52309162) （52309162）
